Exploiting epigenomic and sequence-based features for predicting enhancer-promoter interactions
How to discriminate distal regulatory elements to a gene target is challenging in understanding gene regulation and illustrating causes of complex diseases. Among known distal regulatory elements, enhancers interact with a target gene’s promoter to regulate its expression. Although the emergence of...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2020-01-01
|
| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/78/e3sconf_iseese2020_03046.pdf |
| _version_ | 1818419030426386432 |
|---|---|
| author | Zhou Jianguo Liu Renyang Wu Zifeng Zhang Jintao Liu Junhui |
| author_facet | Zhou Jianguo Liu Renyang Wu Zifeng Zhang Jintao Liu Junhui |
| author_sort | Zhou Jianguo |
| collection | DOAJ |
| description | How to discriminate distal regulatory elements to a gene target is challenging in understanding gene regulation and illustrating causes of complex diseases. Among known distal regulatory elements, enhancers interact with a target gene’s promoter to regulate its expression. Although the emergence of many machine learning approaches has been able to predict enhancer-promoter interactions (EPIs), global and precise prediction of EPIs at the genomic level still requires further exploration.In this paper, we develop an integrated EPIs prediction method, called EpPredictor with improved performance. By using various features of histone modifications, transcription factor binding sites, and DNA sequences among the human genome, a robust supervised machine learning algorithm, named LightGBM, is introduced to predict enhancer-promoter interactions (EPIs). Among six different cell lines, our method effectively predicts the enhancer-promoter interactions (EPIs) and achieves better performance in F1-score and AUC compared to other methods, such as TargetFinder and PEP. |
| first_indexed | 2024-12-14T12:32:05Z |
| format | Article |
| id | doaj.art-cef8112632064e3ba73daa4f1739332e |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-12-14T12:32:05Z |
| publishDate | 2020-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-cef8112632064e3ba73daa4f1739332e2022-12-21T23:01:08ZengEDP SciencesE3S Web of Conferences2267-12422020-01-012180304610.1051/e3sconf/202021803046e3sconf_iseese2020_03046Exploiting epigenomic and sequence-based features for predicting enhancer-promoter interactionsZhou Jianguo0Liu Renyang1Wu Zifeng2Zhang Jintao3Liu Junhui4School of Software, Yunnan UniversitySchool of Information Science and Engineering, Yunnan UniversitySchool of Software, Yunnan UniversitySchool of Software, Yunnan UniversitySchool of Software, Yunnan UniversityHow to discriminate distal regulatory elements to a gene target is challenging in understanding gene regulation and illustrating causes of complex diseases. Among known distal regulatory elements, enhancers interact with a target gene’s promoter to regulate its expression. Although the emergence of many machine learning approaches has been able to predict enhancer-promoter interactions (EPIs), global and precise prediction of EPIs at the genomic level still requires further exploration.In this paper, we develop an integrated EPIs prediction method, called EpPredictor with improved performance. By using various features of histone modifications, transcription factor binding sites, and DNA sequences among the human genome, a robust supervised machine learning algorithm, named LightGBM, is introduced to predict enhancer-promoter interactions (EPIs). Among six different cell lines, our method effectively predicts the enhancer-promoter interactions (EPIs) and achieves better performance in F1-score and AUC compared to other methods, such as TargetFinder and PEP.https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/78/e3sconf_iseese2020_03046.pdf |
| spellingShingle | Zhou Jianguo Liu Renyang Wu Zifeng Zhang Jintao Liu Junhui Exploiting epigenomic and sequence-based features for predicting enhancer-promoter interactions E3S Web of Conferences |
| title | Exploiting epigenomic and sequence-based features for predicting enhancer-promoter interactions |
| title_full | Exploiting epigenomic and sequence-based features for predicting enhancer-promoter interactions |
| title_fullStr | Exploiting epigenomic and sequence-based features for predicting enhancer-promoter interactions |
| title_full_unstemmed | Exploiting epigenomic and sequence-based features for predicting enhancer-promoter interactions |
| title_short | Exploiting epigenomic and sequence-based features for predicting enhancer-promoter interactions |
| title_sort | exploiting epigenomic and sequence based features for predicting enhancer promoter interactions |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/78/e3sconf_iseese2020_03046.pdf |
| work_keys_str_mv | AT zhoujianguo exploitingepigenomicandsequencebasedfeaturesforpredictingenhancerpromoterinteractions AT liurenyang exploitingepigenomicandsequencebasedfeaturesforpredictingenhancerpromoterinteractions AT wuzifeng exploitingepigenomicandsequencebasedfeaturesforpredictingenhancerpromoterinteractions AT zhangjintao exploitingepigenomicandsequencebasedfeaturesforpredictingenhancerpromoterinteractions AT liujunhui exploitingepigenomicandsequencebasedfeaturesforpredictingenhancerpromoterinteractions |